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Estimating parsimonious models of longitudinal causal effects using regressions on propensity scores.

TitleEstimating parsimonious models of longitudinal causal effects using regressions on propensity scores.
Publication TypeJournal Article
Year of Publication2013
AuthorsShinohara RT, Narayan AK, Hong K, Kim HS, Coresh JJ, Streiff MB
Secondary AuthorsFrangakis CE
JournalStat Med
Volume32
Issue22
Pagination3829-37
Date Published2013 Sep 30
ISSN1097-0258
KeywordsAnticoagulants, Data Interpretation, Statistical, Female, Humans, Longitudinal Studies, Male, Middle Aged, Models, Statistical, Neoplasms, Propensity Score, Treatment Outcome, Vena Cava Filters
Abstract

Parsimony is important for the interpretation of causal effect estimates of longitudinal treatments on subsequent outcomes. One method for parsimonious estimates fits marginal structural models by using inverse propensity scores as weights. This method leads to generally large variability that is uncommon in more likelihood-based approaches. A more recent method fits these models by using simulations from a fitted g-computation, but requires the modeling of high-dimensional longitudinal relations that are highly susceptible to misspecification. We propose a new method that, first, uses longitudinal propensity scores as regressors to reduce the dimension of the problem and then uses the approximate likelihood for the first estimates to fit parsimonious models. We demonstrate the methods by estimating the effect of anticoagulant therapy on survival for cancer and non-cancer patients who have inferior vena cava filters.

DOI10.1002/sim.5801
Alternate JournalStat Med
PubMed ID23533091
PubMed Central IDPMC3910397
Grant ListR01 AI102710 / AI / NIAID NIH HHS / United States